Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	132,577
2	South Dakota	129,596
3	Rhode Island	124,119
4	Utah	118,092
5	Arizona	114,496
6	Tennessee	114,035
7	Oklahoma	109,374
8	Iowa	108,809
9	Arkansas	108,376
10	Wisconsin	107,495
11	Nebraska	106,203
12	South Carolina	103,751
13	Alabama	103,652
14	Kansas	103,364
15	Mississippi	101,097
16	Indiana	100,447
17	Idaho	98,359
18	Nevada	97,245
19	Illinois	95,784
20	Wyoming	95,595
21	Montana	95,487
22	Georgia	95,079
23	New Jersey	94,666
24	Texas	94,232
25	Louisiana	94,124
26	Kentucky	94,010
27	Missouri	93,679
28	Delaware	93,055
29	Florida	92,171
30	California	91,764
31	New York	89,893
32	New Mexico	89,891
33	Minnesota	88,354
34	Massachusetts	87,553
35	North Carolina	84,925
36	Ohio	84,723
37	Alaska	82,256
38	Connecticut	82,209
39	Colorado	77,591
40	Pennsylvania	75,986
41	West Virginia	75,706
42	Virginia	69,810
43	Michigan	67,454
44	Maryland	65,210
45	District of Columbia	60,393
46	New Hampshire	57,963
47	Washington	46,394
48	Puerto Rico	42,842
49	Oregon	37,904
50	Maine	35,112
51	Vermont	27,231
52	Hawaii	19,996

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Alabama	375
2	New Jersey	375
3	New York	339
4	Rhode Island	309
5	Delaware	240
6	Connecticut	236
7	Massachusetts	222
8	South Carolina	214
9	Vermont	197
10	Michigan	195
11	Florida	182
12	New Hampshire	181
13	Minnesota	177
14	Alaska	172
15	Colorado	172
16	District of Columbia	161
17	Pennsylvania	160
18	Tennessee	155
19	West Virginia	155
20	Maryland	142
21	Virginia	142
22	Maine	136
23	Idaho	131
24	North Carolina	124
25	Nebraska	123
26	Oklahoma	120
27	Texas	118
28	Montana	116
29	Utah	115
30	South Dakota	113
31	Mississippi	111
32	Kentucky	109
33	Illinois	103
34	Ohio	102
35	Iowa	101
36	Indiana	96
37	Wyoming	94
38	Georgia	93
39	Nevada	87
40	Washington	81
41	New Mexico	80
42	Wisconsin	79
43	Louisiana	77
44	California	75
45	Arizona	71
46	North Dakota	67
47	Puerto Rico	67
48	Missouri	65
49	Arkansas	61
50	Oregon	58
51	Kansas	49
52	Hawaii	36

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,693
2	New York	2,495
3	Rhode Island	2,431
4	Massachusetts	2,419
5	Mississippi	2,319
6	Arizona	2,274
7	Connecticut	2,184
8	South Dakota	2,161
9	Louisiana	2,130
10	Alabama	2,106
11	North Dakota	1,952
12	Pennsylvania	1,923
13	Indiana	1,910
14	New Mexico	1,838
15	Illinois	1,832
16	Arkansas	1,816
17	Iowa	1,788
18	South Carolina	1,724
19	Tennessee	1,689
20	Michigan	1,679
21	Georgia	1,668
22	Nevada	1,662
23	Kansas	1,659
24	Texas	1,610
25	Delaware	1,551
26	Ohio	1,528
27	Florida	1,506
28	District of Columbia	1,476
29	California	1,432
30	Missouri	1,429
31	West Virginia	1,412
32	Maryland	1,332
33	Montana	1,304
34	Wisconsin	1,232
35	Minnesota	1,208
36	Wyoming	1,193
37	Oklahoma	1,188
38	Virginia	1,178
39	Nebraska	1,155
40	Kentucky	1,152
41	North Carolina	1,119
42	Idaho	1,073
43	Colorado	1,062
44	New Hampshire	881
45	Washington	681
46	Puerto Rico	651
47	Utah	632
48	Oregon	553
49	Maine	539
50	Alaska	399
51	Vermont	342
52	Hawaii	316

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	California	10
2	Delaware	5
3	Arkansas	4
4	Kentucky	4
5	Massachusetts	4
6	New York	4
7	Florida	3
8	Louisiana	3
9	Texas	3
10	Virginia	3
11	West Virginia	3
12	Alabama	2
13	Connecticut	2
14	Georgia	2
15	Mississippi	2
16	Nevada	2
17	New Jersey	2
18	Rhode Island	2
19	South Carolina	2
20	Arizona	1
21	District of Columbia	1
22	Idaho	1
23	Illinois	1
24	Indiana	1
25	Iowa	1
26	Kansas	1
27	Maryland	1
28	Michigan	1
29	Missouri	1
30	North Carolina	1
31	Pennsylvania	1
32	South Dakota	1
33	Utah	1
34	Vermont	1
35	Alaska	0
36	Colorado	0
37	Hawaii	0
38	Maine	0
39	Minnesota	0
40	Montana	0
41	Nebraska	0
42	New Hampshire	0
43	New Mexico	0
44	North Dakota	0
45	Ohio	0
46	Oklahoma	0
47	Oregon	0
48	Puerto Rico	0
49	Tennessee	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	346,807	1	99
Chattahoochee	Georgia	303,291	2	99
Bent	Colorado	265,914	3	99
Lincoln	Arkansas	243,397	4	99
Dewey	South Dakota	243,381	5	99
Davidson	Tennessee	132,608	219	93
Richland	South Carolina	102,523	1060	66
York	South Carolina	98,360	1253	60
Orange	California	83,130	1971	37
Pierce	Washington	44,237	2906	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Galax city	Virginia	7,878	3	99
Foard	Texas	7,792	4	99
Emporia city	Virginia	7,669	5	99
Orange	California	1,413	1809	42
Davidson	Tennessee	1,256	2004	36
Richland	South Carolina	1,241	2025	35
York	South Carolina	1,214	2068	34
Pierce	Washington	648	2707	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.